Open Access
RAIRO-Oper. Res.
Volume 57, Number 4, July-August 2023
Page(s) 2177 - 2196
Published online 15 September 2023
  • G. Abbasi, F. Khoshalhan and S.J. Hosseininezhad, Municipal solid waste management and energy production: A multi-objective optimization approach to incineration and biogas waste-to-energy supply chain. Sustain. Energy Tech. Assess. 54 (2022) 102809. [Google Scholar]
  • R.K. Bachar, S. Bhuniya, S.K. Ghosh and B. Sarkar, Controllable energy consumption in a sustainable smart manufacturing model considering superior service, flexible demand, and partial outsourcing. Mathematics 10 (2022) 4517. [CrossRef] [Google Scholar]
  • S.B. Choi, B.K. Dey, S.J. Kim and B. Sarkar, Intelligent servicing strategy for an online-to-offline (O2O) supply chain under demand variability and controllable lead time. RAIRO: Oper. Res. 56 (2022) 1623–1653. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • J. Dong, H.K. Jeswani, A. Nzihou and A. Azapagic, The environmental cost of recovering energy from municipal solid waste. Appl. Energy 267 (2020) 114792. [CrossRef] [Google Scholar]
  • S.N. Emenike and G. Falcone, A review on energy supply chain resilience through optimization. Renew. Sustain. Energy Rev. 134 (2020) 110088. [CrossRef] [Google Scholar]
  • Y. Fernando, P.S. Bee, C.J.C. Jabbou and A.M.T. Thome, Understanding the effects of energy management practices on renewable energy supply chains: Implications for energy policy in emerging economies. Energy policy 118 (2018) 418–428. [CrossRef] [Google Scholar]
  • S. Ghosh, K.H. Kiifer, S.K. Roy and G.W. Weber, Carbon mechanism on sustainable multi-objective solid transportation problem for waste management in Pythagorean hesitant fuzzy environment. Compl. Intell. Sys. 8 (2022) 4115–4143. [CrossRef] [Google Scholar]
  • S.K. Goyal, An integrated inventory model for a single supplier-single customer problem. Int. J. Prod. Res. 15 (1976) 107–111. [Google Scholar]
  • M.S. Habib, M. Omair, M.B. Ramzan, T.N. Chaudhary, M. Farooq and B. Sarkar, A robust possibilistic flexible programming approach toward a resilient and cost-efficient biodiesel supply chain network. J. Clean. Prod. 366 (2022) 132752. [CrossRef] [Google Scholar]
  • S.K. Hota, S.K. Ghosh and B. Sarkar, A solution to the transportation hazard problem in a supply chain with an unreliable manufacturer. AIMS Environ. Sci. 9 (2022) 354–380. [CrossRef] [Google Scholar]
  • C. Hu, X. Liu, J. Lu and C.H. Wang, Distributionally robust optimization for power trading of waste-to-energy plants under uncertainty. Appl. Energy 276 (2020) 115509. [CrossRef] [Google Scholar]
  • M.W. Iqbal and Y. Kang, Waste-to-energy supply chain management with energy feasibility condition. J. Clean. Prod. 291 (2021) 125231. [CrossRef] [Google Scholar]
  • M.H. Iqbal, Y. Kang and W.H. Jeon, Zero waste strategy for green supply chain management with minimization of energy consumption. J. Clean. Prod. 245 (2020) 118827. [CrossRef] [Google Scholar]
  • I. Khan, J. Jemai, H. Lim and B. Sarkar, Effect of electrical energy on the manufacturing setup cost reduction, transportation discounts, and process quality improvement in a two-echelon supply chain management under. Energies 12 (2019) 3733. [CrossRef] [Google Scholar]
  • A.S.H. Kugele, W. Ahmed and B. Sarkar, Geometric programming solution of second degree difficulty for carbon ejection controlled reliable smart production system. RAIRO: Oper. Res. 56 (2022) 1013–1029. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • E. Lakovou, A. Karagiannidis, D. Vlachos, A. Toka and A. Malamakis, Waste biomass-to-energy supply chain management: A critical synthesis. Waste Manag. 30 (2020) 1860–1870. [Google Scholar]
  • A.S. Mahapatra, M.S. Mahapatra, B. Sarkar and S.K. Majumder, Benefit of preservation technology with promotion and time-dependent deterioration under fuzzy learning. Exp. Syst. App. 201 (2022) 117169. [CrossRef] [Google Scholar]
  • U. Mishra, J.Z. Wu and B. Sarkar, A sustainable production-inventory model for a controllable carbon emissions rate under shortages. J. Clean. Prod. 256 (2020) 120268. [CrossRef] [Google Scholar]
  • M. Mittal and B. Sarkar, Stochastic behavior of exchange rate on an international supply chain under random energy price. Math. Comput. Simul. 205 (2023) 232–250. [CrossRef] [Google Scholar]
  • A. Mondal and S.K. Roy, Application of Choquet integral in interval type-2 Pythagorean fuzzy sustainable supply chain management under risk. Int. J. Intell. Syst. 37 (2021) 217–263. [Google Scholar]
  • A.K. Mondal, S. Pareek, K. Chaudhuri, A. Bera, R.K. Bachar and B. Sarkar, Technology license sharing strategy for remanufacturing industries under a closed-loop supply chain management bonding. RAIRO: Oper. Res. 56 (2022) 3017–3045. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • I. Moon, W.Y. Yun and B. Sarkar, Effects of variable setup cost, reliability, and production costs under controlled carbon emissions in a reliable production system. Eur. J. Ind. Eng. 16 (2022) 371–397. [CrossRef] [Google Scholar]
  • T. Mukherjee, I. Sangal, B. Sarkar and T.M. Alkadash, Mathematical estimation for maximum flow of goods within a crossdock to reduce inventory. Math. Biosci. Eng. 19 (2022) 13710–13731. [CrossRef] [Google Scholar]
  • L.J.R. Nunes, T.P. Causer and D. Ciolkosz, Biomass for energy: A review on supply chain management models. Renew. Sustain. Energy Rev. 120 (2019). [Google Scholar]
  • S.V.S. Padiyar, N. Vandana, S.R.Singh Bhagat and B. Sarkar, Joint replenishment strategy for deteriorating multi-item through multi-echelon supply chain model with imperfect production under imprecise and inflationary environment. RAIRO: Oper. Res. 56 (2022) 3071–3096. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • P. Pan, W. Peng, J. Li, H. Chen, G. Xu and T. Liu, Design and evaluation of a conceptual waste-to-energy approach integrating plasma waste gasification with coal-fired power generation. Energy 238 (2021) 121947. [Google Scholar]
  • A. Paul, M. Pervin, S.K. Roy, N. Maculan and G.W. Weber, A green inventory model with the effect of carbon taxation. Ann. Oper. Res. 309 (2022) 233–248. [CrossRef] [MathSciNet] [Google Scholar]
  • M. Pervin, S.K. Roy and G.W. Weber, An integrated vendor-buyer model with quadratic demand under inspection policy and preservation technology. Hacett. J. Math. Stat. 49 (2020) 1168–1189. [CrossRef] [Google Scholar]
  • I.G. Sahebi, A. Mosayebi, B. Masoomi and F. Marandi, Modeling the enablers for blockchain technology adoption in renewable energy supply chain. Tech. Soc. 68 (2022) 101871. [CrossRef] [Google Scholar]
  • B. Sarkar, B. Mridha and S. Pareek, A sustainable smart multi-type biofuel manufacturing with the optimum energy utilization under flexible production. J. Clean. Prod. 332 (2022) 129869. [CrossRef] [Google Scholar]
  • B. Sarkar, D. Takeyeva, R. Guchhait and M. Sarkar, Optimized radio-frequency identification system for different warehouse shapes. Know. Based Syst. 258 (2022) 109811. [CrossRef] [Google Scholar]
  • B. Sarkar, J. Joo, Y. Kim, H. Park and M. Sarkar, Controlling defective items in a complex multi-phase manufacturing system. RAIRO: Oper. Res. 56 (2022) 871–889. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  • A. Sarkar, R. Guchhait and B. Sarkar, Application of the artificial neural network with multithreading within an inventory model under uncertainty and inflation. Int. J. Fuzzy Syst. 24 (2022) 2318–2332. [CrossRef] [Google Scholar]
  • A. Soylu, C. Oruc, M. Turkay, K. Fujita and T. Asakura, Synergy analysis of collaborative supply chain management in energy systems using multi-period MlLP. Eur. J. Oper. Res. 174 (2006) 387–403. [CrossRef] [Google Scholar]
  • O. Taherzadeh, M. Bithell and K. Richards, Water, energy and land insecurity in global supply chains. Global Environ. Change. 67 (2021) 102158. [CrossRef] [Google Scholar]
  • S. Theppitak, D. Hungwe, L. Ding, D. Xin, G. Yu and K. Yoshikawa, Comparison on solid biofuel production from wet and dry carbonization processes of food wastes. Appl. Energy 272 (2020) 115264. [CrossRef] [Google Scholar]
  • Q. Wang, F. Jiang and R. Li, Assessing supply chain greenness from the perspective of embodied renewable energy: A data envelopment analysis using multi-regional input-output analysis. Renew. Energy 189 (2022) 1292–1305. [CrossRef] [Google Scholar]
  • L. Xiang, Energy emergency supply chain collaboration optimization with group consensus through reinforcement learning considering non-cooperative behaviours. Energy 210 (2020) 118597. [CrossRef] [Google Scholar]
  • D. Yadav, R. Singh, A. Kumar and B. Sarkar, Reduction of pollution through sustainable and flexible production by controlling by-products. J. Environ. Inf. 40 (2022) 106–124. [Google Scholar]

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